MULTI-CRITERIA PARTITIONING AND INFLUENCE MAXIMIZATION IN LARGE GRAPHS

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering
PhD Thesis Defence
Title: "MULTI-CRITERIA PARTITIONING AND INFLUENCE MAXIMIZATION IN LARGE GRAPHS"
By
Mr. Eleftherios NTAFLOS
Abstract
Large graphs are prominent in several domains including data management,
computer vision and language modeling. In this thesis, we target common
scenarios related to Geo-Social networks. In particular, we focus on two
fundamental problems: i) multicriteria partitioning in the presence of
constraints and ii) influence maximization for the case of several competitors.
Initially, we introduce a robust agent-based framework that tackles the
Constrained Graph Partitioning (CGP) problem. CGP can be useful for
recommendations of social events, or delivery of targeted advertising material
to certain users. It assigns users of a social network to a set of classes with
bounded capacities so that the similarity and the social costs are minimized.
The similarity cost is proportional to the dis-similarity between a user and
his class, whereas the social cost is measured in terms of friends that are
assigned to different classes. We investigate two solutions for CGP. The first
utilizes a game-theoretic framework, where each user constitutes a player that
wishes to minimize his own social and similarity cost. The second employs local
search, and aims at minimizing the global cost. We show that the two approaches
can be unified under a common agent-based framework that allows for two types
of deviations. In a unilateral deviation an agent switches to a new class,
whereas in a bilateral deviation a pair of agents exchange their classes. We
develop a number of optimization techniques to improve result quality and
facilitate efficiency. Our experimental evaluation on real datasets
demonstrates that the proposed methods always outperform the state-of-the art
in terms of solution quality, while they are up to an order of magnitude
faster.
For the second case, we focus on competitive influence maximization, where
multiple competitors wish to influence the users of a social network; e.g.,
advertisers marketing similar products. In addition, we consider that users
have weights according to their importance (e.g., users whose profile or
demographic characteristics match the advertised product have high weights).
Therefore, we introduce the novel Competitive Weighted Influence Maximization
CWim problem. We first present an Awareness-to-Influence (AtI) model that
distinguishes the concepts of awareness and influence. AtI is motivated by the
fact that usually users do not blindly follow the first influence; instead they
collect information, reproduce it and finally decide. We then prove that AtI
also exhibits monotonicity and submodularity, which facilitate tight quality
guarantees. Next we develop algorithms based on a game theoretic framework,
considering that each competitor is a player that chooses a strategy (i.e.,
seed set) in order to maximize his own benefit. Our experimental findings
suggest that the proposed method outperform considerably some benchmarks and
scale very well to large social networks.
Date: Friday, 7 December 2018
Time: 2:00pm - 4:00pm
Venue: Room 5508
Lifts 25/26
Chairman: Prof. Jeffrey Chasnov (MATH)
Committee Members: Prof. Dimitrios Papadias (Supervisor)
Prof. Frederick Lochovsky
Prof. Raymond Wong
Prof. Ajay Joneja (IDEA)
Prof. Matthias Renz (CAU Kiel)
**** ALL are Welcome ****